Journal cover Journal topic
Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 3.400 IF 3.400
  • IF 5-year value: 3.841 IF 5-year
    3.841
  • CiteScore value: 3.71 CiteScore
    3.71
  • SNIP value: 1.472 SNIP 1.472
  • IPP value: 3.57 IPP 3.57
  • SJR value: 1.770 SJR 1.770
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 70 Scimago H
    index 70
  • h5-index value: 49 h5-index 49
Volume 8, issue 1
Atmos. Meas. Tech., 8, 281–299, 2015
https://doi.org/10.5194/amt-8-281-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Meas. Tech., 8, 281–299, 2015
https://doi.org/10.5194/amt-8-281-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 14 Jan 2015

Research article | 14 Jan 2015

Use of neural networks in ground-based aerosol retrievals from multi-angle spectropolarimetric observations

A. Di Noia et al.
Related authors  
Ensemble-based satellite-derived carbon dioxide and methane column-averaged dry-air mole fraction data sets (2003-2018) for carbon and climate applications
Maximilian Reuter, Michael Buchwitz, Oliver Schneising, Stefan Noel, Heinrich Bovensmann, John P. Burrows, Hartmut Boesch, Antonio Di Noia, Jasdeep Anand, Robert J. Parker, Peter Somkuti, Lianghai Wu, Otto P. Hasekamp, Ilse Aben, Akihiko Kuze, Hiroshi Suto, Kei Shiomi, Yukio Yoshida, Isamu Morino, David Crisp, Christopher O'Dell, Justus Notholt, Christof Petri, Thorsten Warneke, Voltaire Velazco, Nicholas M. Deutscher, David W. T. Griffith, Rigel Kivi, Dave Pollard, Frank Hase, Ralf Sussmann, Yao V. Te, Kimberly Strong, Sebastien Roche, Mahesh K. Sha, Martine De Maziere, Dietrich G. Feist, Laura T. Iraci, Coleen Roehl, Christian Retscher, and Dinand Schepers
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2019-398,https://doi.org/10.5194/amt-2019-398, 2019
Manuscript under review for AMT
Short summary
Aerosol retrievals from the ACEPOL Campaign
Guangliang Fu, Otto Hasekamp, Jeroen Rietjens, Martijn Smit, Antonio Di Noia, Brian Cairns, Andrzej Wasilewski, David Diner, Feng Xu, Kirk Knobelspiesse, Meng Gao, Arlindo da Silva, Sharon Burton, Chris Hostetler, John Hair, and Richard Ferrare
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2019-287,https://doi.org/10.5194/amt-2019-287, 2019
Revised manuscript under review for AMT
Short summary
Retrieval of liquid water cloud properties from POLDER-3 measurements using a neural network ensemble approach
Antonio Di Noia, Otto P. Hasekamp, Bastiaan van Diedenhoven, and Zhibo Zhang
Atmos. Meas. Tech., 12, 1697–1716, https://doi.org/10.5194/amt-12-1697-2019,https://doi.org/10.5194/amt-12-1697-2019, 2019
Short summary
Combined neural network/Phillips–Tikhonov approach to aerosol retrievals over land from the NASA Research Scanning Polarimeter
Antonio Di Noia, Otto P. Hasekamp, Lianghai Wu, Bastiaan van Diedenhoven, Brian Cairns, and John E. Yorks
Atmos. Meas. Tech., 10, 4235–4252, https://doi.org/10.5194/amt-10-4235-2017,https://doi.org/10.5194/amt-10-4235-2017, 2017
Short summary
Atmospheric aerosol characterization with a ground-based SPEX spectropolarimetric instrument
G. van Harten, J. de Boer, J. H. H. Rietjens, A. Di Noia, F. Snik, H. Volten, J. M. Smit, O. P. Hasekamp, J. S. Henzing, and C. U. Keller
Atmos. Meas. Tech., 7, 4341–4351, https://doi.org/10.5194/amt-7-4341-2014,https://doi.org/10.5194/amt-7-4341-2014, 2014
Related subject area  
Subject: Aerosols | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Unified quantitative observation of coexisting volcanic sulfur dioxide and sulfate aerosols using ground-based Fourier transform infrared spectroscopy
Pasquale Sellitto, Henda Guermazi, Elisa Carboni, Richard Siddans, and Mike Burton
Atmos. Meas. Tech., 12, 5381–5389, https://doi.org/10.5194/amt-12-5381-2019,https://doi.org/10.5194/amt-12-5381-2019, 2019
Short summary
Aerosol direct radiative effect over clouds from a synergy of Ozone Monitoring Instrument (OMI) and Moderate Resolution Imaging Spectroradiometer (MODIS) reflectances
Martin de Graaf, L. Gijsbert Tilstra, and Piet Stammes
Atmos. Meas. Tech., 12, 5119–5135, https://doi.org/10.5194/amt-12-5119-2019,https://doi.org/10.5194/amt-12-5119-2019, 2019
Short summary
A Tale of Two Dust Storms: analysis of a complex dust event in the Middle East
Steven D. Miller, Louie D. Grasso, Qijing Bian, Sonia M. Kreidenweis, Jack F. Dostalek, Jeremy E. Solbrig, Jennifer Bukowski, Susan C. van den Heever, Yi Wang, Xiaoguang Xu, Jun Wang, Annette L. Walker, Ting-Chi Wu, Milija Zupanski, Christine Chiu, and Jeffrey S. Reid
Atmos. Meas. Tech., 12, 5101–5118, https://doi.org/10.5194/amt-12-5101-2019,https://doi.org/10.5194/amt-12-5101-2019, 2019
Short summary
Dust mass, cloud condensation nuclei, and ice-nucleating particle profiling with polarization lidar: updated POLIPHON conversion factors from global AERONET analysis
Albert Ansmann, Rodanthi-Elisavet Mamouri, Julian Hofer, Holger Baars, Dietrich Althausen, and Sabur F. Abdullaev
Atmos. Meas. Tech., 12, 4849–4865, https://doi.org/10.5194/amt-12-4849-2019,https://doi.org/10.5194/amt-12-4849-2019, 2019
3+2 + X: what is the most useful depolarization input for retrieving microphysical properties of non-spherical particles from lidar measurements using the spheroid model of Dubovik et al. (2006)?
Matthias Tesche, Alexei Kolgotin, Moritz Haarig, Sharon P. Burton, Richard A. Ferrare, Chris A. Hostetler, and Detlef Müller
Atmos. Meas. Tech., 12, 4421–4437, https://doi.org/10.5194/amt-12-4421-2019,https://doi.org/10.5194/amt-12-4421-2019, 2019
Short summary
Cited articles  
Aires, F., Rossow, W. B., Scott, N. A., and Chédin, A.: Remote sensing from the infrared atmospheric sounding interferometer instrument 1. Compression, denoising, and first-guess retrieval algorithms, J. Geophys. Res., 107, 4619, https://doi.org/10.1029/2001JD000955, 2002.
Alexander, D.: Volcanic ash in the atmosphere and risks for civil aviation: A study in European crisis management, Int. J. Disaster Risk Sci., 4, 9–19, https://doi.org/10.1007/s13753-013-0003-0, 2013.
Anderson, J. O., Thundiyil, J. G., and Stolbach, A.: Clearing the air: A review of the effects of particulate matter air pollution on human health, J. Med. Toxicol., 8, 166–175, https://doi.org/10.1007/s13181-011-0203-1, 2012.
Antonelli, P., Revercomb, H. E., Sromovsky, L. A., Smith, W. L., Knuteson, R. O., Tobin, D. C., Garcia, R. K., Howell, H. B., Huang, H.-L., and Best, F. A.: A principal component noise filter for high spectral resolution infrared measurements, J. Geophys. Res., 109, D23102, https://doi.org/10.1029/2004JD004862, 2004.
Bishop, C. M.: Neural Networks for Pattern Recognition, Oxford University Press, New York, NY, USA, 1995a.
Publications Copernicus
Download
Short summary
A neural network algorithm has been developed to retrieve aerosol microphysical parameters from ground-based measurements of skylight intensity and polarization. The neural network is capable of producing accurate estimates of aerosol optical thicknesses, effective radii and refractive index. In addition, it is shown that the use of the neural retrievals as initial guess for an iterative retrieval algorithm results in improved convergence and retrieval accuracy.
A neural network algorithm has been developed to retrieve aerosol microphysical parameters from...
Citation